During the past 30 yr an impasse has developed in the discovery and commercialization of synthetic herbicides with new molecular targets and novel chemistries. Similarly, there has been little success with bioherbicides, both microbial and chemical. These bioherbicides are needed to combat fast-growing herbicide resistance and to fulfill the need for more environmentally and toxicologically safe herbicides. In response to this substantial and growing opportunity, numerous start-up companies are utilizing novel approaches to provide new tools for weed management. These diverse new tools broaden the scope of discovery, encompassing advanced computational, bioinformatic, and imaging platforms; plant genome–editing and targeted protein degradation technologies; and machine learning and artificial intelligence (AI)-based strategies. This review contains summaries of the presentations of 10 such companies that took part in a symposium held at the WSSA annual meeting in 2024. Four of the companies are developing microbial bioherbicides or natural product–based herbicides, and the other six are using advanced technologies, such as AI, to accelerate the discovery of herbicides with novel molecular target sites or to develop non-GMO, herbicide-resistant crops.

Wetland sediments are valuable archives of environmental change but can be challenging to date. Terrestrial macrofossils are often sparse, resulting in radiocarbon (14C) dating of less desirable organic fractions. An alternative approach for capturing changes in atmospheric 14C is the use of terrestrial microfossils. We 14C date pollen microfossils from two Australian wetland sediment sequences and compare these to ages from other sediment fractions (n = 56). For the Holocene Lake Werri Berri record, pollen 14C ages are consistent with 14C ages on bulk sediment and humic acids (n = 14), whilst Stable Polycyclic Aromatic Carbon (SPAC) 14C ages (n = 4) are significantly younger. For Welsby Lagoon, pollen concentrate 14C ages (n = 21) provide a stratigraphically coherent sequence back to 50 ka BP. 14C ages from humic acid and >100 µm fractions (n = 13) are inconsistent, and often substantially younger than pollen ages. Our comparison of Bayesian age-depth models, developed in Oxcal, Bacon and Undatable, highlight the strengths and weaknesses of the different programs for straightforward and more complex chrono-stratigraphic records. All models display broad similarities but differences in modeled age-uncertainty, particularly when age constraints are sparse. Intensive dating of wetland sequences improves the identification of outliers and generation of robust age models, regardless of program used.

This study describes risk factors associated with mortality among COVID-19 cases reported in the WHO African region between 21 March and 31 October 2020. Average hazard ratios of death were calculated using weighted Cox regression as well as median time to death for key risk factors. We included 46 870 confirmed cases reported by eight Member States in the region. The overall incidence was 20.06 per 100 000, with a total of 803 deaths and a total observation time of 3 959 874 person-days. Male sex (aHR 1.54 (95% CI 1.31–1.81); P < 0.001), older age (aHR 1.08 (95% CI 1.07–1.08); P < 0.001), persons who lived in a capital city (aHR 1.42 (95% CI 1.22–1.65); P < 0.001) and those with one or more comorbidity (aHR 36.37 (95% CI 20.26–65.27); P < 0.001) had a higher hazard of death. Being a healthcare worker reduced the average hazard of death by 40% (aHR 0.59 (95% CI 0.37–0.93); P = 0.024). Time to death was significantly less for persons ≥60 years (P = 0.038) and persons residing in capital cities (P < 0.001). The African region has COVID-19-related mortality similar to that of other regions, and is likely underestimated. Similar risk factors contribute to COVID-19-associated mortality as identified in other regions.

Very diverse Pu compounds exhibit strikingly universal features in their valence-band photoemission (PES) spectra. The conjecture that such features represent the 5f5 final state multiplet has been corroborated by LDA+Hubbard I calculations, meaning that the ground state has a mixed 5f5-5f6 character. Later on, more elaborated DMFT techniques (one crossing approximation, QMC) led to similar conclusions, providing quantitative explanation of such intermeate-valent situation in more details. Analogies in PES spectra of δ-Pu and other Pu systems suggest that the situation envisaged for δ-Pu is relevant for a large group of Pu compounds. Here we show that the around mean field LDA+U in conjunction with the Hubbard I approximation, which describes well the non-magnetic ground state for δ-Pu, captures in reality properties of a large group of Pu (as well as e.g. Am) compounds, reproducing correctly the onset of magnetism and size of magnetic moments.

Specific heat of Pu doped with Am was studied for several Am concentrations (8-20%) ensuring the δ-Pu fcc structure. The results reveal that the γ value of the low temperature specific heat is lower than originally assumed (γ of 35 - 55 mJ/mol K2 can be deduced for Pu-8%Am) and does not increase with the Am-induced lattice expansion. The findings can be taken as an indication of the absence of the Pu-5f states at the Fermi level.